Dynamic linear sibling or cohort regression models were used to predict the 2023 return abundance of the 1.2, 1.3, 2.2, and 2.3 age classes (European age designation). Sibling or more precisely cohort regression models use the return abundance of younger members of the same cohort, that experienced the same conditions at ocean entry, to predict older members of the same cohort. Dynamic linear sibling regression models allow these relationships to evolve over time.

The DLM sibling model predicts the abundance of returning fish from stock s of ocean age a in calendar year t, R(s,t,a), as a function of the return abundance of the same stock in the previous year (t-1) at ocean age a-1: R(s,t,a) = a(t) + b(t)*R(s,t-1,a-1) + e(t) Where a(t) is a dynamic intercept coefficient in year t, describing changes in average production of ocean age a individuals over time, and b(t) describes the relationship between the age classes: a(t) ~ Normal(a(t-1), sigma_a)